Geospatial information is useful to a number of applications, such as targeted advertising, regional sentiment analysis and situational awareness. Due to a lack of sufficient and reliable geographical information in social media (e.g., Internet Protocol (IP) addresses mapped to locations), various geotagging methods have been utilized to infer geographical location based on text data. Such geotagging methods leverage location indicative words to determine location. For instance, by knowing local sports event footy and a local transportation tram, the most probable location inferred is City X, Country Y, because these words together are mostly used by City X residents.
Streaming text data in social media is dynamic, i.e., its content and topics change rapidly, making geotagging a non-trivial task. Existing geotagging models are often trained in an off-line manner, and this implies these models do not capture the temporal variance of geospatial words, when time-invariant geospatial words are persistently associated with a location, while other words are only temporarily associated with a location.